Serving each customer right

Gone are the days when companies simply want to collect as many customers as possible. Today, forward-looking companies deploy sophisticated data mining algorithms to determine which customers are valuable in the long run. After all, it is useful to identify customers who will be overly demanding in terms of service, who will seldom pay their invoices in a timely manner and who will hop from one supplier to the next.

We are working to build a future where all of our clients can access energy resources efficiently and sustainably.

Zdravka Jevtimov
Customer Insights Manager

Eni, an integrated energy company with activities in 69 countries on five continents around the world, is using SAS® Analytics to better understand its customers. Since entering the Belgian market in 2008, the company now offers power and gas services in all market segments: retail, SME and industrial. Today, Eni holds the No. 3 position in the Belgian energy market with more than 800,000 retail connections and 50,000 plus B2B connections.

Client behavior feeds strategic decisions

There is no question that the energy market is highly competitive, and it’s crucial for a company like Eni to build long-term relationships with its clients.

“We are working to build a future where all of our clients can access energy resources efficiently and sustainably,” says Zdravka Jevtimov, Customer Insights Manager at Eni. “My job is to monitor, analyze and understand the behavior of our entire client base, as well as each individual client. This is precisely the type of information that helps our management make the right strategic decisions.”

Predicting a customer’s long-term value

Every client is important to Eni, and the company wants to make sure it can offer each of them the best possible service. “On the one hand, we want to strengthen our relationship with customers who have a positive long-term value so that they will remain customers,” Jevtimov says. “On the other hand, we want to understand why certain customers have a low or negative estimated value, so we can do whatever’s in our means to try and turn them into profitable clients.

“We calculate how much the customer will spend with us (revenues) and how long they will stay (retention). We also predict when we might experience payment issues (credit losses) with them and how much it will cost us to serve their needs (service costs).

“This may all sound rather straightforward, but the development of a solid and trustworthy prediction model isn’t created overnight. In our case, we teamed up with Python Predictions. Together, we identified the essential components that define the long-term value, and we developed more than 700 parameters to build the predictive models. We took the time to discuss all of this extensively with every department within our company, as it was essential that we had everybody 100 percent on board.”

Countless possibilities for integrating rough data

Building robust predictive models efficiently requires a smart, powerful and flexible software solution. Wouter Buckinx, co-founder and managing partner at Python Predictions, explains how SAS came into the picture.

“We were a SAS partner right from the inception of our company in 2006, and our client Eni was a seasoned SAS user as well,” Buckinx says. “More importantly, SAS has shown that it knows how to really dig deep into data and locate all the richest veins. SAS solutions offer countless possibilities for integrating rough data, and this is equally true for building accurate formulas. We have found that the flexibility of the SAS software is beyond comparison in these areas.”

Increasing profitability

SAS Analytics not only offers Eni’s management valuable information about customers, it also helps them evaluate the future profitability of the company’s product portfolio, sales channels and customer segments. In addition, analytics enables management to run “what if” scenarios, allowing them to assess the impact of strategic decisions, such as changes in price or margin, reduced customer churn and more. These KPIs are monitored through user-friendly dashboards, so Eni is in a perfect position to make well-funded decisions that will help increase the long-term profitability of the entire customer base.

All set for the future

While Eni’s powerful SAS analysis tool brings the company all the information it needs, it’s also designed for future expansion. This is important because the amount of available data will rapidly grow in the foreseeable future.

“We intend to keep on investing in analytics to grow our business,” Jevtimov says. “In this respect, we welcome the introduction of smart energy solutions that will create new opportunities to understand and serve our clients even better. Therefore, we made sure that our SAS analytical systems and our people are armed and ready to cope with these developments and the challenges that come with them.”

Challenge

Determine the expected long-term value of each client and understand the drivers behind it.

Solution

Benefits

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.